Name
Abstract | ||
|---|---|---|
Linear discriminant analysis (LDA) as a well-known supervised dimensionality reduction method has been widely applied in many fields. However, the lack of sparsity in the LDA solution makes interpretation of the results challenging. In this paper, we propose a new model for sparse uncorrelated LDA (ULDA). Our model is based on the characterization of all solutions of the generalized ULDA. We incor... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TNNLS.2015.2448637 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Optimization,Feature extraction,Sparse matrices,Computational modeling,Linear discriminant analysis,Acceleration,Matrix decomposition | Dimensionality reduction,Pattern recognition,Matrix decomposition,Uncorrelated,Orthogonality,Feature extraction,Bregman method,Artificial intelligence,Linear discriminant analysis,Sparse matrix,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
27 | 7 | 2162-237X |
Citations | PageRank | References |
5 | 0.38 | 29 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaowei Zhang | 1 | 5 | 0.38 |
| Delin Chu | 2 | 24 | 2.72 |
| Roger C. E. Tan | 3 | 102 | 19.13 |